The need for computing DRRs (digitally reconstructed radiographs) arises in a number of situations. In image-guided surgery and radio-surgery, for example, DRRs from CT (computed tomography) scans of a patient are correlated with live patient images in order to determine the patient displacement from the desired position for administering the surgical treatment. Typically, the generation of a DRR for each CT orientation involves a large number of computations.
Speed is of essence when the DRRs are computed in real time, for example in order to determine the patient position, and in order to dynamically correct for patient displacement during the course of the surgical or radiosurgical treatment. Even when a library of DRRs is generated offline in order to choose a best match for the live images from the DRRs, fast DRR generation will allow for higher accuracy by generating an adequate number of DRRs to achieve the desired accuracy for determining the patient position.
Several algorithms for achieving fast DRR generation have been proposed. For example, in one known method an intermediate representation of the attenuations through the 3D CT volume, called a transgraph, is used to speed up the computation of DRRs. In the transgraph method, only a limited number of the transgraphs are computed upfront, however, so that the resulting DRRs are approximations for most positions of interest. Further, the range of orientations for which the DRRs can be computed is severely limited.
There is a need for a method and system for rapidly generating DRRs (or other forms of reconstructed 2D images) from 3D scan data.